Objective

To make a recommendation system that recommends at least five(5) new products based on the user's habits.

Context

Everyday a million products are being recommended to users based on popularity and other metrics on e-commerce websites. The most popular e-commerce website boosts average order value by 50%, increases revenues by 300%, and improves conversion. In addition to being a powerful tool for increasing revenues, product recommendations are so essential that customers now expect to see similar features on all other eCommerce sites.

Data Description -

Data columns- First three columns are userId, productId, and ratings and the fourth column is timestamp. You can discard the timestamp column as in this case you may not need to use it.

Source

Amazon Reviews data (http://jmcauley.ucsd.edu/data/amazon/) The repository has several datasets. For this case study, we are using the Electronics dataset.